import os
import numpy as np
import rasterio
import matplotlib.pyplot as plt
from glob import glob

folder = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB/"
tif_files = sorted(glob(os.path.join(folder, 'AMB_*.tif')))

# Importar librerías necesarias
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
import cv2
import matplotlib.pyplot as plt

# Configuración inicial
IMAGE_SIZE = 256  # Tamaño al que redimensionaremos las imágenes y máscaras
BATCH_SIZE = 20  # Tamaño del lote
EPOCHS = 50 # Número de épocas
LEARNING_RATE = 1e-4 # Tasa de aprendizaje

def plot_image_with_cropmask(tif_path):
    with rasterio.open(tif_path) as src:
        img = src.read()  # (bands, rows, cols)
        profile = src.profile

    # Asignar bandas RGB
    rgb = np.stack([img[0], img[1], img[2]], axis=-1)  # B4, B3, B2
    rgb = np.clip(rgb, 0, 10000) / 10000  # Normalizar

    crop = img[3]  # Banda EUCROPMAP

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))
    ax1.imshow(rgb)
    ax1.set_title('Imagen RGB')
    ax1.axis('off')

    ax2.imshow(crop, cmap='tab20', interpolation='none')
    ax2.set_title('Máscara EUCROPMAP')
    ax2.axis('off')

    plt.suptitle(os.path.basename(tif_path))
    plt.tight_layout()
    plt.show()

if tif_files:
    plot_image_with_cropmask(tif_files[0])

import pandas as pd

# Lista para guardar los resultados
estadisticas = []
imagenes = []

# Procesar cada imagen
for tif in tif_files:
    with rasterio.open(tif) as src:
        img = src.read()
        imagenes.append((os.path.basename(tif), img))

    crop = img[3]
    clases, counts = np.unique(crop, return_counts=True) # Obtener las clases
    total = counts.sum()

    for clase, count in zip(clases, counts):
        estadisticas.append({ 
            'archivo': os.path.basename(tif),
            'clase': int(clase), # Clase de la máscara EUCROPMAP
            'pixeles': int(count), # Número de píxeles
            'porcentaje': round(100 * count / total, 2) # Porcentaje de píxeles
        })

# Crear DataFrame
df_stats = pd.DataFrame(estadisticas)
df_stats.head()

df_resumen = df_stats.pivot_table(index='archivo', columns='clase', values='porcentaje', fill_value=0)
df_resumen = df_resumen.round(2)

# Mostrar el DataFrame resumen
df_resumen

df_resumen.plot(kind='bar', stacked=True, figsize=(14, 6), colormap='tab20')
plt.title("Distribución porcentual de clases EUCROPMAP por celda (AMB)")
plt.ylabel("Porcentaje (%)")
plt.xlabel("Archivo")
plt.xticks(rotation=45)
plt.legend(title='Clase', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True)
plt.tight_layout()
plt.show()

# Extraer celda y año del nombre del archivo
df_stats['celda'] = df_stats['archivo'].str.extract(r'(R\d+_C\d+)')
df_stats['anio'] = df_stats['archivo'].str.extract(r'_(\d{4})')

# Pivot para ver cómo cambia cada clase en el tiempo por celda
pivot_tiempo = df_stats.pivot_table(index=['celda', 'anio'], columns='clase', values='porcentaje', fill_value=0)
pivot_tiempo.head()

# Elegir una celda para analizar (por ejemplo 'R0_C0')
celda_objetivo = 'R0_C1'

# Comprobar si la celda está disponible
if celda_objetivo in pivot_tiempo.index.get_level_values('celda'):
    pivot_tiempo.loc[celda_objetivo].T.plot(figsize=(10, 6), marker='o')
    plt.title(f"Evolución de clases EUCROPMAP - {celda_objetivo}")
    plt.ylabel("Porcentaje (%)")
    plt.xlabel("Clase")
    plt.grid(True)
    plt.legend(title="Año", bbox_to_anchor=(1.05, 1), loc='upper left')
    plt.tight_layout()
    plt.show()
else:
    print(f"La celda {celda_objetivo} no está disponible en los datos.")

import numpy as np
import rasterio
import os
from glob import glob
from skimage.transform import resize

# Carpeta
# Configuración de directorios
IMAGENES_DIR = r"C:/Users/crome/Desktop/VIU/Code/Datos/AMB/images"
MASCARAS_DIR = r"C:/Users/crome/Desktop/VIU/Code/Datos/AMB/masks"

tif_files = sorted(glob("C:/Users/crome/Desktop/VIU/Code/Datos/AMB/*.tif"))
out_dir = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB/npy_files/"

os.makedirs(IMAGENES_DIR, exist_ok=True)
os.makedirs(MASCARAS_DIR, exist_ok=True)


target_rgb_shape = (256, 256, 3)
target_mask_shape = (256, 256)

errores = []

for tif_path in tif_files:
    name = os.path.basename(tif_path).replace('.tif', '')

    try:
        with rasterio.open(tif_path) as src:
            img = src.read()

        rgb = np.stack([img[0], img[1], img[2]], axis=-1).astype(np.float32) / 10000.0
        mask = img[3].astype(np.uint8)

        # Redimensionar
        rgb = resize(rgb, target_rgb_shape, preserve_range=True, anti_aliasing=True)
        mask = resize(mask, target_mask_shape, order=0, preserve_range=True).astype(np.uint8)

        np.save(os.path.join(IMAGENES_DIR, f'{name}_rgb.npy'), rgb)
        np.save(os.path.join(MASCARAS_DIR, f'{name}_mask.npy'), mask)

    except Exception as e:
        print(f"Error procesando {name}: {e}")
        errores.append(name)

print("Conversión completada.")
if errores:
    print("Errores en archivos:", errores)

Conversión completada.

# Imprimir ejemplo de un archivo convertido
example_file = os.path.join(IMAGENES_DIR, 'AMB_R0_C1_2018_rgb.npy')
if os.path.exists(example_file):
    example_rgb = np.load(example_file)
    plt.imshow(example_rgb)
    plt.title('Ejemplo de imagen RGB convertida')
    plt.axis('off')
    plt.show()
# Imprimir ejemplo de una máscara convertida
example_mask_file = os.path.join(MASCARAS_DIR, 'AMB_R0_C1_2018_mask.npy')
if os.path.exists(example_mask_file):
    example_mask = np.load(example_mask_file)
    plt.imshow(example_mask, cmap='tab20', interpolation='none')
    plt.title('Ejemplo de máscara convertida')
    plt.axis('off')
    plt.show()

from tensorflow.keras import Input, Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Conv2DTranspose, concatenate
from tensorflow.keras.optimizers import Adam

def unet_model(input_size=(256, 256, 3), num_classes=10):
    inputs = Input(input_size)

    # Encoder
    c1 = Conv2D(32, (3,3), activation='relu', padding='same')(inputs)
    c1 = Conv2D(32, (3,3), activation='relu', padding='same')(c1)
    p1 = MaxPooling2D((2,2))(c1)

    c2 = Conv2D(64, (3,3), activation='relu', padding='same')(p1)
    c2 = Conv2D(64, (3,3), activation='relu', padding='same')(c2)
    p2 = MaxPooling2D((2,2))(c2)

    c3 = Conv2D(128, (3,3), activation='relu', padding='same')(p2)
    c3 = Conv2D(128, (3,3), activation='relu', padding='same')(c3)
    p3 = MaxPooling2D((2,2))(c3)

    # Bottleneck
    c4 = Conv2D(256, (3,3), activation='relu', padding='same')(p3)
    c4 = Conv2D(256, (3,3), activation='relu', padding='same')(c4)

    # Decoder
    u5 = Conv2DTranspose(128, (2,2), strides=(2,2), padding='same')(c4)
    u5 = concatenate([u5, c3])
    c5 = Conv2D(128, (3,3), activation='relu', padding='same')(u5)
    c5 = Conv2D(128, (3,3), activation='relu', padding='same')(c5)

    u6 = Conv2DTranspose(64, (2,2), strides=(2,2), padding='same')(c5)
    u6 = concatenate([u6, c2])
    c6 = Conv2D(64, (3,3), activation='relu', padding='same')(u6)
    c6 = Conv2D(64, (3,3), activation='relu', padding='same')(c6)

    u7 = Conv2DTranspose(32, (2,2), strides=(2,2), padding='same')(c6)
    u7 = concatenate([u7, c1])
    c7 = Conv2D(32, (3,3), activation='relu', padding='same')(u7)
    c7 = Conv2D(32, (3,3), activation='relu', padding='same')(c7)

    outputs = Conv2D(num_classes, (1,1), activation='softmax')(c7)

    model = Model(inputs=[inputs], outputs=[outputs])
    model.compile(optimizer=Adam(learning_rate=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])

    return model

import numpy as np
import os
from glob import glob
from skimage.transform import resize

# Rutas
folder = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB/unet/"
os.makedirs(folder, exist_ok=True)

rgb_files = sorted(glob(os.path.join(IMAGENES_DIR, '*_rgb.npy')))
mask_files = sorted(glob(os.path.join(MASCARAS_DIR, '*_mask.npy')))

X = []
y = []

# Tamaños estándar
target_rgb_shape = (256, 256, 3)
target_mask_shape = (256, 256)

for rgb_file, mask_file in zip(rgb_files, mask_files):
    # Cargar imagen RGB
    rgb = np.load(rgb_file).astype(np.float32)

    # Normalización automática
    max_val = rgb.max()
    if max_val > 1000:
        rgb /= 10000.0
    elif max_val > 1.1:
        rgb /= 255.0
    # Si ya está entre 0-1, no se hace nada

    # Cargar máscara
    mask = np.load(mask_file).astype(np.uint8)

    # Redimensionar si es necesario
    if rgb.shape != target_rgb_shape:
        rgb = resize(rgb, target_rgb_shape, preserve_range=True, anti_aliasing=True)
    if mask.shape != target_mask_shape:
        mask = resize(mask, target_mask_shape, preserve_range=True, order=0).astype(np.uint8)

    X.append(rgb)
    y.append(mask)

# Convertir a arrays finales
X = np.stack(X)
y = np.stack(y)

# Guardar
np.save(os.path.join(folder, 'X_AMB_2018.npy'), X)
np.save(os.path.join(folder, 'y_AMB_2018.npy'), y)

print("Datos combinados y guardados:")
print("X shape:", X.shape)
print("y shape:", y.shape)

Datos combinados y guardados:
X shape: (99, 256, 256, 3)
y shape: (99, 256, 256)

import matplotlib.pyplot as plt
import numpy as np
import os

# Ruta donde se han guardado
folder = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB/unet/"

# Cargar arrays combinados
X = np.load(os.path.join(folder, 'X_AMB_2018.npy'))
y = np.load(os.path.join(folder, 'y_AMB_2018.npy'))

# Ver ejemplo (por ejemplo el primero)
i = 0  # índice de la imagen que quieres ver

plt.figure(figsize=(10, 4))

plt.subplot(1, 2, 1)
plt.imshow(X[i])
plt.title('Imagen RGB')
plt.axis('off')

plt.subplot(1, 2, 2)
plt.imshow(y[i], cmap='tab20', interpolation='none')
plt.title('Máscara EUCROPMAP')
plt.axis('off')

plt.tight_layout()
plt.show()

import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split

# Cargar datos
folder = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB/unet/"
X = np.load(folder + 'X_AMB_2018.npy')
y = np.load(folder + 'y_AMB_2018.npy')

# Asegurar que esté en formato int
y = y.astype(np.int32)

# Crear un mapeo de etiquetas únicas a índices continuos
clases_unicas = np.unique(y)
clase_a_indice = {clase: idx for idx, clase in enumerate(clases_unicas)}
indice_a_clase = {idx: clase for clase, idx in clase_a_indice.items()} 

# Aplicar mapeo
y_reindex = np.vectorize(clase_a_indice.get)(y)

# Convertir a one-hot
n_classes = len(clases_unicas)
y_cat = tf.keras.utils.to_categorical(y_reindex, num_classes=n_classes)

# División
X_train, X_val, y_train, y_val = train_test_split(X, y_cat, test_size=0.2, random_state=42)

print("Clases mapeadas:", clase_a_indice)
print("X_train:", X_train.shape, "y_train:", y_train.shape)

Clases mapeadas: {0: 0, 34: 1, 44: 2, 88: 3, 100: 4, 211: 5, 212: 6, 213: 7, 215: 8, 216: 9, 221: 10, 223: 11, 230: 12, 231: 13, 232: 14, 240: 15, 244: 16, 250: 17}
X_train: (79, 256, 256, 3) y_train: (79, 256, 256, 18)

from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping

model = unet_model(input_size=(256, 256, 3), num_classes=n_classes)

# Callbacks
checkpoint = ModelCheckpoint("mejor_modelo.h5", monitor="val_loss", save_best_only=True)
early_stop = EarlyStopping(monitor="val_loss", patience=10, restore_best_weights=True)

history = model.fit(
    X_train, y_train,
    validation_data=(X_val, y_val),
    batch_size=8,
    epochs=100,
    callbacks=[checkpoint, early_stop]
)

Epoch 1/100
10/10 [==============================] - 29s 3s/step - loss: 2.8846 - accuracy: 0.3880 - val_loss: 2.8760 - val_accuracy: 0.5786
Epoch 2/100
10/10 [==============================] - 27s 3s/step - loss: 2.8710 - accuracy: 0.5461 - val_loss: 2.8556 - val_accuracy: 0.4463
Epoch 3/100
10/10 [==============================] - 28s 3s/step - loss: 2.8394 - accuracy: 0.4901 - val_loss: 2.7793 - val_accuracy: 0.4675
Epoch 4/100
10/10 [==============================] - 26s 3s/step - loss: 2.5579 - accuracy: 0.5000 - val_loss: 1.7456 - val_accuracy: 0.4666
Epoch 5/100
10/10 [==============================] - 25s 3s/step - loss: 1.4529 - accuracy: 0.4702 - val_loss: 1.5070 - val_accuracy: 0.4585
Epoch 6/100
10/10 [==============================] - 25s 3s/step - loss: 1.1854 - accuracy: 0.4596 - val_loss: 1.2860 - val_accuracy: 0.4681
Epoch 7/100
10/10 [==============================] - 25s 3s/step - loss: 1.1460 - accuracy: 0.5008 - val_loss: 1.2841 - val_accuracy: 0.4677
Epoch 8/100
10/10 [==============================] - 25s 3s/step - loss: 1.0998 - accuracy: 0.4851 - val_loss: 1.2058 - val_accuracy: 0.4630
Epoch 9/100
10/10 [==============================] - 26s 3s/step - loss: 1.0440 - accuracy: 0.4936 - val_loss: 1.1891 - val_accuracy: 0.4724
Epoch 10/100
10/10 [==============================] - 25s 3s/step - loss: 1.0173 - accuracy: 0.5170 - val_loss: 1.1425 - val_accuracy: 0.4694
Epoch 11/100
10/10 [==============================] - 27s 3s/step - loss: 0.9810 - accuracy: 0.5155 - val_loss: 1.0842 - val_accuracy: 0.4686
Epoch 12/100
10/10 [==============================] - 25s 3s/step - loss: 0.9554 - accuracy: 0.5141 - val_loss: 1.0549 - val_accuracy: 0.4691
Epoch 13/100
10/10 [==============================] - 36s 4s/step - loss: 0.9298 - accuracy: 0.5166 - val_loss: 1.0381 - val_accuracy: 0.4697
Epoch 14/100
10/10 [==============================] - 41s 4s/step - loss: 0.9019 - accuracy: 0.5160 - val_loss: 1.0091 - val_accuracy: 0.4691
Epoch 15/100
10/10 [==============================] - 41s 4s/step - loss: 0.8633 - accuracy: 0.5168 - val_loss: 0.9858 - val_accuracy: 0.4700
Epoch 16/100
10/10 [==============================] - 43s 4s/step - loss: 0.8189 - accuracy: 0.5170 - val_loss: 0.9554 - val_accuracy: 0.4700
Epoch 17/100
10/10 [==============================] - 41s 4s/step - loss: 0.7755 - accuracy: 0.5171 - val_loss: 0.9302 - val_accuracy: 0.4697
Epoch 18/100
10/10 [==============================] - 45s 5s/step - loss: 0.7423 - accuracy: 0.5167 - val_loss: 0.9069 - val_accuracy: 0.4705
Epoch 19/100
10/10 [==============================] - 42s 4s/step - loss: 0.7251 - accuracy: 0.5173 - val_loss: 0.8940 - val_accuracy: 0.4703
Epoch 20/100
10/10 [==============================] - 41s 4s/step - loss: 0.7121 - accuracy: 0.5161 - val_loss: 0.8944 - val_accuracy: 0.4701
Epoch 21/100
10/10 [==============================] - 41s 4s/step - loss: 0.7072 - accuracy: 0.5179 - val_loss: 0.8806 - val_accuracy: 0.4585
Epoch 22/100
10/10 [==============================] - 41s 4s/step - loss: 0.7323 - accuracy: 0.5203 - val_loss: 0.9199 - val_accuracy: 0.4777
Epoch 23/100
10/10 [==============================] - 41s 4s/step - loss: 0.7340 - accuracy: 0.5200 - val_loss: 0.9098 - val_accuracy: 0.4807
Epoch 24/100
10/10 [==============================] - 40s 4s/step - loss: 0.7056 - accuracy: 0.5219 - val_loss: 0.8765 - val_accuracy: 0.4770
Epoch 25/100
10/10 [==============================] - 42s 4s/step - loss: 0.6897 - accuracy: 0.5429 - val_loss: 0.8681 - val_accuracy: 0.4989
Epoch 26/100
10/10 [==============================] - 40s 4s/step - loss: 0.6799 - accuracy: 0.5663 - val_loss: 0.8692 - val_accuracy: 0.5619
Epoch 27/100
10/10 [==============================] - 41s 4s/step - loss: 0.6724 - accuracy: 0.6343 - val_loss: 0.8546 - val_accuracy: 0.5955
Epoch 28/100
10/10 [==============================] - 41s 4s/step - loss: 0.6812 - accuracy: 0.6585 - val_loss: 0.9453 - val_accuracy: 0.4702
Epoch 29/100
10/10 [==============================] - 41s 4s/step - loss: 0.7909 - accuracy: 0.5175 - val_loss: 0.9447 - val_accuracy: 0.4702
Epoch 30/100
10/10 [==============================] - 41s 4s/step - loss: 0.7515 - accuracy: 0.5570 - val_loss: 0.8981 - val_accuracy: 0.5232
Epoch 31/100
10/10 [==============================] - 43s 4s/step - loss: 0.7038 - accuracy: 0.6209 - val_loss: 0.8575 - val_accuracy: 0.5999
Epoch 32/100
10/10 [==============================] - 41s 4s/step - loss: 0.6661 - accuracy: 0.7222 - val_loss: 0.8509 - val_accuracy: 0.6057
Epoch 33/100
10/10 [==============================] - 41s 4s/step - loss: 0.6595 - accuracy: 0.7024 - val_loss: 0.8659 - val_accuracy: 0.6219
Epoch 34/100
10/10 [==============================] - 42s 4s/step - loss: 0.6575 - accuracy: 0.7515 - val_loss: 0.8493 - val_accuracy: 0.6043
Epoch 35/100
10/10 [==============================] - 41s 4s/step - loss: 0.6625 - accuracy: 0.6860 - val_loss: 0.8407 - val_accuracy: 0.6347
Epoch 36/100
10/10 [==============================] - 41s 4s/step - loss: 0.6453 - accuracy: 0.7287 - val_loss: 0.8232 - val_accuracy: 0.6870
Epoch 37/100
10/10 [==============================] - 41s 4s/step - loss: 0.6157 - accuracy: 0.7835 - val_loss: 0.8065 - val_accuracy: 0.6912
Epoch 38/100
10/10 [==============================] - 41s 4s/step - loss: 0.5961 - accuracy: 0.7854 - val_loss: 0.7941 - val_accuracy: 0.6906
Epoch 39/100
10/10 [==============================] - 41s 4s/step - loss: 0.5830 - accuracy: 0.7861 - val_loss: 0.7836 - val_accuracy: 0.6926
Epoch 40/100
10/10 [==============================] - 42s 4s/step - loss: 0.5729 - accuracy: 0.7884 - val_loss: 0.7794 - val_accuracy: 0.6937
Epoch 41/100
10/10 [==============================] - 41s 4s/step - loss: 0.5608 - accuracy: 0.7890 - val_loss: 0.7645 - val_accuracy: 0.6947
Epoch 42/100
10/10 [==============================] - 41s 4s/step - loss: 0.5530 - accuracy: 0.7896 - val_loss: 0.7616 - val_accuracy: 0.6936
Epoch 43/100
10/10 [==============================] - 41s 4s/step - loss: 0.5554 - accuracy: 0.7882 - val_loss: 0.7522 - val_accuracy: 0.6951
Epoch 44/100
10/10 [==============================] - 41s 4s/step - loss: 0.5468 - accuracy: 0.7890 - val_loss: 0.7520 - val_accuracy: 0.6946
Epoch 45/100
10/10 [==============================] - 41s 4s/step - loss: 0.5442 - accuracy: 0.7887 - val_loss: 0.7462 - val_accuracy: 0.6960
Epoch 46/100
10/10 [==============================] - 41s 4s/step - loss: 0.5352 - accuracy: 0.7909 - val_loss: 0.7393 - val_accuracy: 0.6965
Epoch 47/100
10/10 [==============================] - 42s 4s/step - loss: 0.5323 - accuracy: 0.7911 - val_loss: 0.7341 - val_accuracy: 0.6967
Epoch 48/100
10/10 [==============================] - 41s 4s/step - loss: 0.5302 - accuracy: 0.7912 - val_loss: 0.7366 - val_accuracy: 0.6968
Epoch 49/100
10/10 [==============================] - 41s 4s/step - loss: 0.5283 - accuracy: 0.7912 - val_loss: 0.7347 - val_accuracy: 0.6969
Epoch 50/100
10/10 [==============================] - 41s 4s/step - loss: 0.5259 - accuracy: 0.7912 - val_loss: 0.7244 - val_accuracy: 0.6969
Epoch 51/100
10/10 [==============================] - 41s 4s/step - loss: 0.5259 - accuracy: 0.7914 - val_loss: 0.7212 - val_accuracy: 0.6971
Epoch 52/100
10/10 [==============================] - 40s 4s/step - loss: 0.5276 - accuracy: 0.7913 - val_loss: 0.7431 - val_accuracy: 0.6965
Epoch 53/100
10/10 [==============================] - 40s 4s/step - loss: 0.5250 - accuracy: 0.7913 - val_loss: 0.7382 - val_accuracy: 0.6967
Epoch 54/100
10/10 [==============================] - 41s 4s/step - loss: 0.5246 - accuracy: 0.7915 - val_loss: 0.7234 - val_accuracy: 0.6971
Epoch 55/100
10/10 [==============================] - 42s 4s/step - loss: 0.5152 - accuracy: 0.7914 - val_loss: 0.7064 - val_accuracy: 0.6974
Epoch 56/100
10/10 [==============================] - 41s 4s/step - loss: 0.5092 - accuracy: 0.7917 - val_loss: 0.6952 - val_accuracy: 0.7004
Epoch 57/100
10/10 [==============================] - 41s 4s/step - loss: 0.4982 - accuracy: 0.8088 - val_loss: 0.6827 - val_accuracy: 0.7357
Epoch 58/100
10/10 [==============================] - 41s 4s/step - loss: 0.4849 - accuracy: 0.8171 - val_loss: 0.6867 - val_accuracy: 0.7285
Epoch 59/100
10/10 [==============================] - 40s 4s/step - loss: 0.4781 - accuracy: 0.8190 - val_loss: 0.6687 - val_accuracy: 0.7469
Epoch 60/100
10/10 [==============================] - 41s 4s/step - loss: 0.4692 - accuracy: 0.8280 - val_loss: 0.6784 - val_accuracy: 0.7375
Epoch 61/100
10/10 [==============================] - 41s 4s/step - loss: 0.4659 - accuracy: 0.8282 - val_loss: 0.6764 - val_accuracy: 0.7451
Epoch 62/100
10/10 [==============================] - 42s 4s/step - loss: 0.4606 - accuracy: 0.8355 - val_loss: 0.6509 - val_accuracy: 0.7565
Epoch 63/100
10/10 [==============================] - 41s 4s/step - loss: 0.4625 - accuracy: 0.8371 - val_loss: 0.6624 - val_accuracy: 0.7518
Epoch 64/100
10/10 [==============================] - 42s 4s/step - loss: 0.4712 - accuracy: 0.8255 - val_loss: 0.6584 - val_accuracy: 0.7543
Epoch 65/100
10/10 [==============================] - 40s 4s/step - loss: 0.4653 - accuracy: 0.8209 - val_loss: 0.6617 - val_accuracy: 0.7631
Epoch 66/100
10/10 [==============================] - 41s 4s/step - loss: 0.4613 - accuracy: 0.8370 - val_loss: 0.6558 - val_accuracy: 0.7534
Epoch 67/100
10/10 [==============================] - 40s 4s/step - loss: 0.4544 - accuracy: 0.8372 - val_loss: 0.6691 - val_accuracy: 0.7535
Epoch 68/100
10/10 [==============================] - 40s 4s/step - loss: 0.4484 - accuracy: 0.8373 - val_loss: 0.6627 - val_accuracy: 0.7600
Epoch 69/100
10/10 [==============================] - 41s 4s/step - loss: 0.4547 - accuracy: 0.8391 - val_loss: 0.6537 - val_accuracy: 0.7654
Epoch 70/100
10/10 [==============================] - 42s 4s/step - loss: 0.4513 - accuracy: 0.8383 - val_loss: 0.6490 - val_accuracy: 0.7563
Epoch 71/100
10/10 [==============================] - 40s 4s/step - loss: 0.4481 - accuracy: 0.8380 - val_loss: 0.6337 - val_accuracy: 0.7727
Epoch 72/100
10/10 [==============================] - 41s 4s/step - loss: 0.4421 - accuracy: 0.8418 - val_loss: 0.6427 - val_accuracy: 0.7733
Epoch 73/100
10/10 [==============================] - 41s 4s/step - loss: 0.4454 - accuracy: 0.8436 - val_loss: 0.6337 - val_accuracy: 0.7786
Epoch 74/100
10/10 [==============================] - 40s 4s/step - loss: 0.4380 - accuracy: 0.8495 - val_loss: 0.6531 - val_accuracy: 0.7754
Epoch 75/100
10/10 [==============================] - 41s 4s/step - loss: 0.4394 - accuracy: 0.8491 - val_loss: 0.6403 - val_accuracy: 0.7704
Epoch 76/100
10/10 [==============================] - 41s 4s/step - loss: 0.4318 - accuracy: 0.8502 - val_loss: 0.6517 - val_accuracy: 0.7664
Epoch 77/100
10/10 [==============================] - 41s 4s/step - loss: 0.4234 - accuracy: 0.8533 - val_loss: 0.6241 - val_accuracy: 0.7791
Epoch 78/100
10/10 [==============================] - 43s 4s/step - loss: 0.4236 - accuracy: 0.8520 - val_loss: 0.6230 - val_accuracy: 0.7772
Epoch 79/100
10/10 [==============================] - 41s 4s/step - loss: 0.4175 - accuracy: 0.8561 - val_loss: 0.6514 - val_accuracy: 0.7642
Epoch 80/100
10/10 [==============================] - 40s 4s/step - loss: 0.4217 - accuracy: 0.8520 - val_loss: 0.6029 - val_accuracy: 0.7882
Epoch 81/100
10/10 [==============================] - 39s 4s/step - loss: 0.4135 - accuracy: 0.8574 - val_loss: 0.5927 - val_accuracy: 0.7965
Epoch 82/100
10/10 [==============================] - 40s 4s/step - loss: 0.4094 - accuracy: 0.8581 - val_loss: 0.5881 - val_accuracy: 0.7973
Epoch 83/100
10/10 [==============================] - 39s 4s/step - loss: 0.4084 - accuracy: 0.8579 - val_loss: 0.6295 - val_accuracy: 0.7844
Epoch 84/100
10/10 [==============================] - 40s 4s/step - loss: 0.4109 - accuracy: 0.8574 - val_loss: 0.5910 - val_accuracy: 0.7951
Epoch 85/100
10/10 [==============================] - 40s 4s/step - loss: 0.4049 - accuracy: 0.8591 - val_loss: 0.5860 - val_accuracy: 0.7971
Epoch 86/100
10/10 [==============================] - 40s 4s/step - loss: 0.4086 - accuracy: 0.8575 - val_loss: 0.6104 - val_accuracy: 0.7947
Epoch 87/100
10/10 [==============================] - 41s 4s/step - loss: 0.4049 - accuracy: 0.8599 - val_loss: 0.6092 - val_accuracy: 0.7954
Epoch 88/100
10/10 [==============================] - 41s 4s/step - loss: 0.4048 - accuracy: 0.8597 - val_loss: 0.5877 - val_accuracy: 0.7982
Epoch 89/100
10/10 [==============================] - 41s 4s/step - loss: 0.4064 - accuracy: 0.8607 - val_loss: 0.5789 - val_accuracy: 0.8003
Epoch 90/100
10/10 [==============================] - 40s 4s/step - loss: 0.3982 - accuracy: 0.8627 - val_loss: 0.5788 - val_accuracy: 0.8009
Epoch 91/100
10/10 [==============================] - 40s 4s/step - loss: 0.3972 - accuracy: 0.8609 - val_loss: 0.5875 - val_accuracy: 0.7971
Epoch 92/100
10/10 [==============================] - 41s 4s/step - loss: 0.3943 - accuracy: 0.8618 - val_loss: 0.5751 - val_accuracy: 0.8002
Epoch 93/100
10/10 [==============================] - 40s 4s/step - loss: 0.3908 - accuracy: 0.8633 - val_loss: 0.5744 - val_accuracy: 0.8014
Epoch 94/100
10/10 [==============================] - 41s 4s/step - loss: 0.3896 - accuracy: 0.8643 - val_loss: 0.5737 - val_accuracy: 0.8026
Epoch 95/100
10/10 [==============================] - 40s 4s/step - loss: 0.3882 - accuracy: 0.8642 - val_loss: 0.5712 - val_accuracy: 0.8025
Epoch 96/100
10/10 [==============================] - 41s 4s/step - loss: 0.3869 - accuracy: 0.8649 - val_loss: 0.5703 - val_accuracy: 0.8023
Epoch 97/100
10/10 [==============================] - 41s 4s/step - loss: 0.3906 - accuracy: 0.8619 - val_loss: 0.5789 - val_accuracy: 0.7993
Epoch 98/100
10/10 [==============================] - 41s 4s/step - loss: 0.3897 - accuracy: 0.8630 - val_loss: 0.5724 - val_accuracy: 0.8000
Epoch 99/100
10/10 [==============================] - 41s 4s/step - loss: 0.3860 - accuracy: 0.8650 - val_loss: 0.5692 - val_accuracy: 0.8025
Epoch 100/100
10/10 [==============================] - 41s 4s/step - loss: 0.3858 - accuracy: 0.8646 - val_loss: 0.5791 - val_accuracy: 0.8008

# Guardar el modelo final
model.save("modelo_final.h5")

# Gráfica de accuracy y loss
plt.figure(figsize=(12, 5))

plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='Train acc')
plt.plot(history.history['val_accuracy'], label='Val acc')
plt.title('Precisión')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='Train loss')
plt.plot(history.history['val_loss'], label='Val loss')
plt.title('Pérdida')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.tight_layout()
plt.show()

# Cargar el mejor modelo
from tensorflow.keras.models import load_model
model = load_model("mejor_modelo.h5")

# Evaluación del Mejor Modelo
loss, accuracy = model.evaluate(X_val, y_val)
print(f"Pérdida en validación: {loss:.4f}")
print(f"Precisión en validación: {accuracy:.4f}")

1/1 [==============================] - 3s 3s/step - loss: 0.5692 - accuracy: 0.8025
Pérdida en validación: 0.5692
Precisión en validación: 0.8025
1/1 [==============================] - 3s 3s/step - loss: 0.5692 - accuracy: 0.8025
Pérdida en validación: 0.5692
Precisión en validación: 0.8025

import numpy as np
from sklearn.metrics import confusion_matrix

def calcular_metricas_segmentacion(y_true, y_pred, n_clases):
    """
    Calcula métricas de segmentación (IoU, Dice y Accuracy) por clase.

    y_true: (N, H, W) ground truth, valores enteros por píxel
    y_pred: (N, H, W) predicción del modelo, valores enteros por píxel
    n_clases: número total de clases (de 0 a n_clases-1)
    """
    assert y_true.shape == y_pred.shape, "Tamaño inconsistente"

    iou_list = []
    dice_list = []
    accuracy_list = []

    print(f"{'Clase':<8} {'IoU':>10} {'Dice':>10} {'Accuracy':>10}")
    print("-" * 40)

    for clase in range(n_clases):
        gt = (y_true == clase)
        pred = (y_pred == clase)

        intersection = np.logical_and(gt, pred).sum()
        union = np.logical_or(gt, pred).sum()
        total_pred = pred.sum()
        total_gt = gt.sum()

        iou = intersection / union if union != 0 else 0.0
        dice = 2 * intersection / (total_pred + total_gt) if (total_pred + total_gt) != 0 else 0.0
        accuracy = intersection / total_gt if total_gt != 0 else 0.0

        iou_list.append(iou)
        dice_list.append(dice)
        accuracy_list.append(accuracy)

        print(f"{clase:<8} {iou:10.4f} {dice:10.4f} {accuracy:10.4f}")

    print("-" * 40)
    print(f"{'PROMEDIO':<8} {np.mean(iou_list):10.4f} {np.mean(dice_list):10.4f} {np.mean(accuracy_list):10.4f}")
    return iou_list, dice_list, accuracy_list


y_val_int = np.argmax(y_val, axis=-1)
y_pred_raw = model.predict(X_val)
y_pred_int = np.argmax(y_pred_raw, axis=-1)
n_clases = len(np.unique(y_val_int))

iou, dice, acc = calcular_metricas_segmentacion(y_val_int, y_pred_int, n_clases)

1/1 [==============================] - 2s 2s/step
1/1 [==============================] - 2s 2s/step
Clase           IoU       Dice   Accuracy
----------------------------------------
0            0.7587     0.8628     0.8690
1            0.0000     0.0000     0.0000
2            0.6755     0.8063     0.8676
3            0.0000     0.0000     0.0000
4            0.0000     0.0000     0.0000
5            0.0000     0.0000     0.0000
6            0.0000     0.0000     0.0000
7            0.0000     0.0000     0.0000
8            0.0000     0.0000     0.0000
9            0.0000     0.0000     0.0000
10           0.0000     0.0000     0.0000
11           0.0000     0.0000     0.0000
12           0.0000     0.0000     0.0000
13           0.0000     0.0000     0.0000
14           0.0000     0.0000     0.0000
15           0.0000     0.0000     0.0000
16           0.0000     0.0000     0.0000
17           0.0000     0.0000     0.0000
----------------------------------------
PROMEDIO     0.0797     0.0927     0.0965

# Predicción sobre una imagen del conjunto de validación
i = 10
pred_mask = model.predict(np.expand_dims(X_val[i], axis=0))[0]
pred_mask_argmax = np.argmax(pred_mask, axis=-1)

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,5))
ax1.imshow(X_val[i])
ax1.set_title("RGB")
ax2.imshow(np.argmax(y_val[i], axis=-1))
ax2.set_title("Máscara real")
ax3.imshow(pred_mask_argmax)
ax3.set_title("Predicción")
plt.tight_layout()
plt.show()

1/1 [==============================] - 0s 248ms/step

# Predicción sobre una imagen del conjunto de validación
i = 2
pred_mask = model.predict(np.expand_dims(X_val[i], axis=0))[0]
pred_mask_argmax = np.argmax(pred_mask, axis=-1)

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,5))
ax1.imshow(X_val[i])
ax1.set_title("RGB")
ax2.imshow(np.argmax(y_val[i], axis=-1))
ax2.set_title("Máscara real")
ax3.imshow(pred_mask_argmax)
ax3.set_title("Predicción")
plt.tight_layout()
plt.show()

1/1 [==============================] - 0s 132ms/step

import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras import backend as K
import pandas as pd
import os
import gc


batch_sizes = [8, 16, 32]
epoch_list = [25, 50, 100]

resultados = []

for batch in batch_sizes:
    for epochs in epoch_list:
        print(f"\n Entrenando con batch_size={batch}, epochs={epochs}")
        
        # Liberar memoria previa
        K.clear_session()
        gc.collect()

        # Crear nuevo modelo desde cero
        model = unet_model(input_size=(256, 256, 3), num_classes=n_classes)
        
        # Callbacks
        nombre_modelo = f"model_bs{batch}_ep{epochs}.h5"
        checkpoint = ModelCheckpoint(nombre_modelo, monitor="val_loss", save_best_only=True, verbose=0)
        early_stop = EarlyStopping(monitor="val_loss", patience=10, restore_best_weights=True, verbose=0)

        history = model.fit(
            X_train, y_train,
            validation_data=(X_val, y_val),
            batch_size=batch,
            epochs=epochs,
            callbacks=[checkpoint, early_stop],
            verbose=1
        )

        best_epoch = np.argmin(history.history['val_loss']) + 1
        final_val_loss = np.min(history.history['val_loss'])
        final_val_acc = history.history['val_accuracy'][best_epoch - 1]

        resultados.append({
            'batch_size': batch,
            'epochs': epochs,
            'best_epoch': best_epoch,
            'val_loss': round(final_val_loss, 4),
            'val_accuracy': round(final_val_acc, 4),
            'modelo': nombre_modelo
        })

# Mostrar resultados en tabla ordenada
df_resultados = pd.DataFrame(resultados)
df_resultados = df_resultados.sort_values(by='val_loss')
print("\n Resultados ordenados por menor pérdida en validación:\n")
print(df_resultados)

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model

# Cargar modelo entrenado
model = load_model("mejor_modelo.h5")

# Función para predecir la máscara
def predict_mask(img):
    pred = model.predict(np.expand_dims(img, axis=0))[0]
    return np.argmax(pred, axis=-1)

# Cargar dos imágenes de la misma zona (fechas distintas)
img_2020 = np.load("C:/Users/crome/Desktop/VIU/Code/Datos/AMB_TEMP/images/AMB_R0_C1_2020_rgb.npy")
img_2023 = np.load("C:/Users/crome/Desktop/VIU/Code/Datos/AMB_TEMP/images/AMB_R0_C1_2024_rgb.npy")

# Normalizar si no están ya
if img_2020.max() > 1: img_2020 = img_2020 / 10000.0
if img_2023.max() > 1: img_2023 = img_2023 / 10000.0

# Predecir
pred_2020 = predict_mask(img_2020)
pred_2023 = predict_mask(img_2023)

# Mapa de cambio
cambio = pred_2020 != pred_2023

# Visualización
fig, axs = plt.subplots(1, 4, figsize=(18, 5))
axs[0].imshow(img_2020)
axs[0].set_title("Imagen 2020")
axs[1].imshow(pred_2020, cmap="tab20")
axs[1].set_title("Máscara 2020")
axs[2].imshow(pred_2023, cmap="tab20")
axs[2].set_title("Máscara 2024")
axs[3].imshow(cambio, cmap='gray')
axs[3].set_title("Cambio detectado")
for ax in axs: ax.axis('off')
plt.tight_layout()
plt.show()

# Estadística
n_cambiados = np.sum(cambio)
total = cambio.size
print(f"Porcentaje de píxeles cambiados: {100 * n_cambiados / total:.2f}%")

1/1 [==============================] - 0s 280ms/step
1/1 [==============================] - 0s 97ms/step

Porcentaje de píxeles cambiados: 3.04%

clase_eucropmap = {
    0:  "Sin información / fondo",
    34: "Cereal – Trigo (soft wheat)",
    44: "Cereal – Cebada (barley)",
    88: "Cereal – Maíz (corn)",
    100: "Cereal – Otros cereales",
    211: "Leguminosas – Guisante (pea)",
    212: "Leguminosas – Alfalfa",
    213: "Leguminosas – Otros",
    215: "Tubérculos – Patata (potato)",
    216: "Tubérculos – Remolacha",
    221: "Forraje – Praderas/perennes",
    223: "Forraje – Cultivos de corte",
    230: "Aceite – Colza (rapeseed)",
    231: "Aceite – Girasol",
    232: "Aceite – Otros oleaginosos",
    240: "Huerto/Frutales – Viñedo",
    244: "Huerto/Frutales – Frutales de pepita/drupe",
    250: "Otros – Superficies no agrícolas"
}

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

def evaluar_segmentacion(y_true, y_pred, n_clases, clase_a_nombre=None):
    """
    Evalúa la segmentación comparando máscaras reales y predichas.
    Muestra IoU por clase, precisión por clase y visualizaciones.
    
    Args:
        y_true: np.array (H, W) máscara real (valores de 0 a n_clases-1)
        y_pred: np.array (H, W) predicción (valores de 0 a n_clases-1)
        n_clases: número total de clases
        clase_a_nombre: dict opcional para mapear índice a nombre
    """

    iou_por_clase = []
    precision_por_clase = []

    print("Métricas por clase:")
    print("-" * 30)

    for clase in range(n_clases):
        interseccion = np.logical_and(y_true == clase, y_pred == clase).sum()
        union = np.logical_or(y_true == clase, y_pred == clase).sum()
        total_pred = (y_pred == clase).sum()

        iou = interseccion / union if union != 0 else 0.0
        precision = interseccion / total_pred if total_pred != 0 else 0.0

        iou_por_clase.append(iou)
        precision_por_clase.append(precision)

        nombre = clase_a_nombre.get(clase, str(clase)) if clase_a_nombre else str(clase)
        print(f"Clase {nombre}: IoU = {iou:.3f}, Precisión = {precision:.3f}")

    # Visualización por clase
    fig, axs = plt.subplots(n_clases, 3, figsize=(10, 4 * n_clases))

    for clase in range(n_clases):
        nombre = clase_a_nombre.get(clase, f"Clase {clase}") if clase_a_nombre else f"Clase {clase}"

        axs[clase, 0].imshow(y_true == clase, cmap='gray')
        axs[clase, 0].set_title(f"Máscara real - {nombre}")

        axs[clase, 1].imshow(y_pred == clase, cmap='gray')
        axs[clase, 1].set_title(f"Predicción - {nombre}")

        axs[clase, 2].imshow(np.logical_and(y_true == clase, y_pred == clase), cmap='Greens')
        axs[clase, 2].set_title(f"Aciertos - {nombre}")

        for j in range(3):
            axs[clase, j].axis('off')

    plt.tight_layout()
    plt.show()

    return iou_por_clase, precision_por_clase

import rasterio
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os

folder = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB/"
output_folder = os.path.join(folder, "rgb_yolo")
os.makedirs(output_folder, exist_ok=True)

for tif_file in sorted(glob(os.path.join(folder, 'AMB_*.tif'))):
    with rasterio.open(tif_file) as src:
        img = src.read([1, 2, 3])  # B4, B3, B2 (ajustar si es Sentinel)
        img = np.clip(img, 0, 10000) / 10000.0
        img = (img.transpose(1, 2, 0) * 255).astype(np.uint8)

        name = os.path.basename(tif_file).replace('.tif', '.jpg')
        Image.fromarray(img).save(os.path.join(output_folder, name))

import os
import xml.etree.ElementTree as ET
import json

# Carpeta base
CARPETA = r"C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo"

def convertir_xml_a_json(path_xml):
    tree = ET.parse(path_xml)
    root = tree.getroot()

    filename = root.find("filename").text
    size = root.find("size")
    width = int(size.find("width").text)
    height = int(size.find("height").text)

    anotaciones = []
    for obj in root.findall("object"):
        label = obj.find("name").text
        bndbox = obj.find("bndbox")
        xmin = int(bndbox.find("xmin").text)
        ymin = int(bndbox.find("ymin").text)
        xmax = int(bndbox.find("xmax").text)
        ymax = int(bndbox.find("ymax").text)

        # Coordenadas centradas (como tus otros JSON)
        x_center = (xmin + xmax) / 2
        y_center = (ymin + ymax) / 2
        width_box = xmax - xmin
        height_box = ymax - ymin

        anotaciones.append({
            "label": label,
            "coordinates": {
                "x": x_center,
                "y": y_center,
                "width": width_box,
                "height": height_box
            }
        })

    json_data = [{
        "image": filename,
        "verified": False,
        "annotations": anotaciones
    }]

    # Guardar como .json
    json_path = os.path.splitext(path_xml)[0] + ".json"
    with open(json_path, "w") as f:
        json.dump(json_data, f, indent=4)

    # Eliminar el XML original
    os.remove(path_xml)
    print(f"Convertido y eliminado: {os.path.basename(path_xml)}")

# Recorre carpeta y convierte todos los .xml
for archivo in os.listdir(CARPETA):
    if archivo.endswith(".xml"):
        convertir_xml_a_json(os.path.join(CARPETA, archivo))

print("Conversión completa.")

Convertido y eliminado: AMB_R0_C1_2018.xml
Convertido y eliminado: AMB_R0_C2_2018.xml
Convertido y eliminado: AMB_R0_C3_2018.xml
Convertido y eliminado: AMB_R1_C0_2018.xml
Convertido y eliminado: AMB_R1_C1_2018.xml
Convertido y eliminado: AMB_R1_C2_2018.xml
Convertido y eliminado: AMB_R1_C3_2018.xml
Convertido y eliminado: AMB_R1_C4_2018.xml
Convertido y eliminado: AMB_R2_C0_2018.xml
Convertido y eliminado: AMB_R2_C1_2018.xml
Convertido y eliminado: AMB_R2_C2_2018.xml
Convertido y eliminado: AMB_R2_C3_2018.xml
Convertido y eliminado: AMB_R2_C4_2018.xml
Convertido y eliminado: AMB_R3_C0_2018.xml
Convertido y eliminado: AMB_R3_C1_2018.xml
Convertido y eliminado: AMB_R3_C2_2018.xml
Convertido y eliminado: AMB_R3_C3_2018.xml
Convertido y eliminado: AMB_R3_C4_2018.xml
Convertido y eliminado: AMB_R3_C5_2018.xml
Convertido y eliminado: AMB_R4_C0_2018.xml
Convertido y eliminado: AMB_R4_C1_2018.xml
Convertido y eliminado: AMB_R4_C2_2018.xml
Convertido y eliminado: AMB_R4_C3_2018.xml
Convertido y eliminado: AMB_R4_C4_2018.xml
Convertido y eliminado: AMB_R4_C5_2018.xml
Convertido y eliminado: AMB_R4_C6_2018.xml
Convertido y eliminado: AMB_R5_C0_2018.xml
Convertido y eliminado: AMB_R5_C1_2018.xml
Convertido y eliminado: AMB_R5_C2_2018.xml
Convertido y eliminado: AMB_R5_C3_2018.xml
Convertido y eliminado: AMB_R5_C4_2018.xml
Convertido y eliminado: AMB_R5_C5_2018.xml
Convertido y eliminado: AMB_R5_C6_2018.xml
Convertido y eliminado: AMB_R5_C7_2018.xml
Convertido y eliminado: AMB_R5_C8_2018.xml
Convertido y eliminado: AMB_R6_C0_2018.xml
Convertido y eliminado: AMB_R6_C1_2018.xml
Convertido y eliminado: AMB_R6_C2_2018.xml
Convertido y eliminado: AMB_R6_C3_2018.xml
Convertido y eliminado: AMB_R6_C4_2018.xml
Convertido y eliminado: AMB_R6_C5_2018.xml
Convertido y eliminado: AMB_R6_C6_2018.xml
Conversión completa.

import os
import json
from collections import Counter

# Ruta a la carpeta donde están los .json
CARPETA = r"C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo"

# Contador para las clases
clases = Counter()

# Recorrer todos los archivos .json
for archivo in os.listdir(CARPETA):
    if archivo.endswith(".json"):
        ruta = os.path.join(CARPETA, archivo)
        try:
            with open(ruta, 'r') as f:
                data = json.load(f)
                if not data or "annotations" not in data[0]:
                    continue

                for ann in data[0]["annotations"]:
                    label = ann["label"]
                    clases[label] += 1
        except Exception as e:
            print(f"Error procesando {archivo}: {e}")

# Mostrar resultados
print("\nClases encontradas:")
for clase in sorted(clases):
    print(f"- {clase}: {clases[clase]} anotaciones")

print(f"\nTotal de clases distintas: {len(clases)}")

Clases encontradas:
- Agricola: 50 anotaciones
- Industria: 118 anotaciones
- Poblacion: 151 anotaciones

Total de clases distintas: 3

import os
import xml.etree.ElementTree as ET
import json
from PIL import Image

# Ruta base
base_dir = r"C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo"
output_dir = r"C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo\txt"

import os
import json

# Ruta de los .json
CARPETA_JSON = r"C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo"

# Conjunto para clases únicas
clases_unicas = set()

# Buscar todas las clases en los .json
for archivo in os.listdir(CARPETA_JSON):
    if archivo.endswith(".json"):
        try:
            with open(os.path.join(CARPETA_JSON, archivo), "r") as f:
                data = json.load(f)
                if not data or "annotations" not in data[0]:
                    continue

                for ann in data[0]["annotations"]:
                    label = ann["label"].strip()
                    clases_unicas.add(label)
        except Exception as e:
            print(f"Error leyendo {archivo}: {e}")

# Ordenar alfabéticamente (opcional) o por aparición
clases_ordenadas = sorted(clases_unicas)  # usa list(clases_unicas) para orden por aparición

# Crear diccionario clase → ID
clase_a_id = {clase: idx for idx, clase in enumerate(clases_ordenadas)}

# Mostrar resultado
print("Diccionario generado:\n")
print("clase_a_id = {")
for k, v in clase_a_id.items():
    print(f"    \"{k}\": {v},")
print("}")

# Función para convertir XML (PASCAL VOC)
def convertir_xml(xml_path):
    img_path = xml_path.replace(".xml", ".jpg")
    txt_path = xml_path.replace(".xml", ".txt")

    if not os.path.exists(img_path):
        print(f"Imagen no encontrada para {xml_path}")
        return

    img = Image.open(img_path)
    w, h = img.size

    tree = ET.parse(xml_path)
    root = tree.getroot()

    yolo_lines = []
    for obj in root.findall("object"):
        label = obj.find("name").text
        if label not in clase_a_id:
            continue
        clase = clase_a_id[label]

        bndbox = obj.find("bndbox")
        xmin = int(bndbox.find("xmin").text)
        ymin = int(bndbox.find("ymin").text)
        xmax = int(bndbox.find("xmax").text)
        ymax = int(bndbox.find("ymax").text)

        x_center = (xmin + xmax) / 2 / w
        y_center = (ymin + ymax) / 2 / h
        box_width = (xmax - xmin) / w
        box_height = (ymax - ymin) / h

        yolo_lines.append(f"{clase} {x_center:.6f} {y_center:.6f} {box_width:.6f} {box_height:.6f}")

    with open(txt_path, "w") as f:
        f.write("\n".join(yolo_lines))
    print(f"Convertido XML: {os.path.basename(txt_path)}")

# Función para convertir JSON
def convertir_json(json_path):
    img_path = json_path.replace(".json", ".jpg")
    txt_path = json_path.replace(".json", ".txt")

    if not os.path.exists(img_path):
        print(f"Imagen no encontrada para {json_path}")
        return

    img = Image.open(img_path)
    w, h = img.size

    with open(json_path, "r") as f:
        data = json.load(f)

    yolo_lines = []
    for ann in data[0].get("annotations", []):
        label = ann["label"]
        if label not in clase_a_id:
            continue
        clase = clase_a_id[label]

        coords = ann["coordinates"]
        x_center = coords["x"] / w
        y_center = coords["y"] / h
        width = coords["width"] / w
        height = coords["height"] / h

        yolo_lines.append(f"{clase} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}")

    with open(txt_path, "w") as f:
        f.write("\n".join(yolo_lines))
    print(f"Convertido JSON: {os.path.basename(txt_path)}")

# Recorremos todos los archivos de la carpeta
for file in os.listdir(base_dir):
    full_path = os.path.join(base_dir, file)

    if file.endswith(".xml"):
        convertir_xml(full_path)
    elif file.endswith(".json"):
        convertir_json(full_path)

print("Conversión completa.")

Diccionario generado:

clase_a_id = {
    "Agricola": 0,
    "Industria": 1,
    "Poblacion": 2,
}
Convertido JSON: AMB_R0_C1_2018.txt
Convertido JSON: AMB_R0_C2_2018.txt
Convertido JSON: AMB_R0_C3_2018.txt
Convertido JSON: AMB_R1_C0_2018.txt
Convertido JSON: AMB_R1_C1_2018.txt
Convertido JSON: AMB_R1_C2_2018.txt
Convertido JSON: AMB_R1_C3_2018.txt
Convertido JSON: AMB_R1_C4_2018.txt
Convertido JSON: AMB_R2_C0_2018.txt
Convertido JSON: AMB_R2_C1_2018.txt
Convertido JSON: AMB_R2_C2_2018.txt
Convertido JSON: AMB_R2_C3_2018.txt
Convertido JSON: AMB_R2_C4_2018.txt
Convertido JSON: AMB_R3_C0_2018.txt
Convertido JSON: AMB_R3_C1_2018.txt
Convertido JSON: AMB_R3_C2_2018.txt
Convertido JSON: AMB_R3_C3_2018.txt
Convertido JSON: AMB_R3_C4_2018.txt
Convertido JSON: AMB_R3_C5_2018.txt
Convertido JSON: AMB_R4_C0_2018.txt
Convertido JSON: AMB_R4_C1_2018.txt
Convertido JSON: AMB_R4_C2_2018.txt
Convertido JSON: AMB_R4_C3_2018.txt
Convertido JSON: AMB_R4_C4_2018.txt
Convertido JSON: AMB_R4_C5_2018.txt
Convertido JSON: AMB_R4_C6_2018.txt
Convertido JSON: AMB_R5_C0_2018.txt
Convertido JSON: AMB_R5_C1_2018.txt
Convertido JSON: AMB_R5_C2_2018.txt
Convertido JSON: AMB_R5_C3_2018.txt
Convertido JSON: AMB_R5_C4_2018.txt
Convertido JSON: AMB_R5_C5_2018.txt
Convertido JSON: AMB_R5_C6_2018.txt
Convertido JSON: AMB_R5_C7_2018.txt
Convertido JSON: AMB_R5_C8_2018.txt
Convertido JSON: AMB_R6_C0_2018.txt
Convertido JSON: AMB_R6_C1_2018.txt
Convertido JSON: AMB_R6_C2_2018.txt
Convertido JSON: AMB_R6_C3_2018.txt
Convertido JSON: AMB_R6_C4_2018.txt
Convertido JSON: AMB_R6_C5_2018.txt
Convertido JSON: AMB_R6_C6_2018.txt
Convertido JSON: AMB_R6_C7_2018.txt
Convertido JSON: AMB_R6_C8_2018.txt
Convertido JSON: AMB_R6_C9_2018.txt
Convertido JSON: AMB_R7_C0_2018.txt
Convertido JSON: AMB_R7_C1_2018.txt
Convertido JSON: AMB_R7_C2_2018.txt
Convertido JSON: AMB_R7_C3_2018.txt
Convertido JSON: AMB_R7_C4_2018.txt
Convertido JSON: AMB_R7_C5_2018.txt
Convertido JSON: AMB_R7_C6_2018.txt
Convertido JSON: AMB_R7_C9_2018.txt
Convertido JSON: AMB_R8_C1_2018.txt
Convertido JSON: AMB_R8_C2_2018.txt
Convertido JSON: AMB_R8_C4_2018.txt
Convertido JSON: AMB_R8_C5_2018.txt
Convertido JSON: AMB_R8_C6_2018.txt
Convertido JSON: AMB_R8_C7_2018.txt
Convertido JSON: AMB_R8_C9_2018.txt
Convertido JSON: AMB_R9_C2_2018.txt
Convertido JSON: AMB_R9_C3_2018.txt
Convertido JSON: AMB_R9_C4_2018.txt
Convertido JSON: AMB_R9_C5_2018.txt
Convertido JSON: AMB_R9_C6_2018.txt
Convertido JSON: AMB_R9_C7_2018.txt
Convertido JSON: AMB_R9_C8_2018.txt
Convertido JSON: AMB_R9_C9_2018.txt
Conversión completa.

import os
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image

# Ruta base
IMG_NAME = "AMB_R2_C1_2018.jpg"  # Cambia por el que quieras
IMG_DIR = r"C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo"
TXT_DIR = IMG_DIR  # Si los .txt están en el mismo sitio

# Leer imagen
img_path = os.path.join(IMG_DIR, IMG_NAME)
img = Image.open(img_path)
w, h = img.size

# Leer anotaciones YOLO
txt_name = os.path.splitext(IMG_NAME)[0] + ".txt"
txt_path = os.path.join(TXT_DIR, txt_name)

boxes = []
with open(txt_path, "r") as f:
    for line in f:
        clase_id, x, y, w_box, h_box = map(float, line.strip().split())
        boxes.append((clase_id, x, y, w_box, h_box))

# Mostrar imagen
fig, ax = plt.subplots(1, figsize=(10, 8))
ax.imshow(img)

# Dibujar cada caja
for clase_id, x, y, w_box, h_box in boxes:
    # Convertir a coordenadas absolutas
    x_abs = (x - w_box / 2) * w
    y_abs = (y - h_box / 2) * h
    width = w_box * w
    height = h_box * h

    rect = patches.Rectangle(
        (x_abs, y_abs),
        width,
        height,
        linewidth=2,
        edgecolor='red',
        facecolor='none'
    )
    ax.add_patch(rect)
    ax.text(x_abs, y_abs - 5, f"Clase {int(clase_id)}", color='yellow', fontsize=10, backgroundcolor='black')

ax.set_title(f"Anotaciones YOLO: {IMG_NAME}")
plt.axis('off')
plt.tight_layout()
plt.show()

import os
import shutil
import random

def dividir_desde_raiz(carpeta_raiz, carpeta_train, carpeta_val, val_ratio=0.2):
    # Crear carpetas destino
    for base in [carpeta_train, carpeta_val]:
        os.makedirs(os.path.join(base, "images"), exist_ok=True)
        os.makedirs(os.path.join(base, "labels"), exist_ok=True)

    # Buscar imágenes con .txt
    archivos = os.listdir(carpeta_raiz)
    imagenes = [
        f for f in archivos
        if f.lower().endswith((".jpg", ".jpeg", ".png"))
        and os.path.exists(os.path.join(carpeta_raiz, f.rsplit(".", 1)[0] + ".txt"))
    ]

    print(f"Encontradas {len(imagenes)} imágenes con sus .txt")

    random.shuffle(imagenes)
    val_size = int(len(imagenes) * val_ratio)

    for i, img in enumerate(imagenes):
        base_name = os.path.splitext(img)[0]
        label_file = base_name + ".txt"

        destino = carpeta_val if i < val_size else carpeta_train

        # Copiar imagen
        shutil.copy(os.path.join(carpeta_raiz, img), os.path.join(destino, "images", img))

        # Copiar etiqueta
        shutil.copy(os.path.join(carpeta_raiz, label_file), os.path.join(destino, "labels", label_file))

    print(f"Dataset dividido: {len(imagenes) - val_size} entrenamiento, {val_size} validación")

# 📂 Carpetas
CARPETA_RAIZ = r"C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo"
CARPETA_TRAIN = os.path.join(CARPETA_RAIZ, "train")
CARPETA_VAL = os.path.join(CARPETA_RAIZ, "val")

# Ejecutar
dividir_desde_raiz(CARPETA_RAIZ, CARPETA_TRAIN, CARPETA_VAL)

Encontradas 68 imágenes con sus .txt
Dataset dividido: 55 entrenamiento, 13 validación

import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
from ultralytics import YOLO

def entrenar_yolo(data_path, modelo_salida, epochs=1000, img_size=640):
    """
    Entrena un modelo YOLOv8 con el dataset proporcionado.
    """
    modelo = YOLO("yolov8n.pt")
    modelo.train(
        data=data_path,
        epochs=epochs,
        imgsz=img_size,
        save_period=10,
        project=os.path.dirname(modelo_salida),
        name=os.path.basename(modelo_salida).split('.')[0]
    )

DATA_PATH = r"data_other_pc.yaml"
MODELO_SALIDA = r"yolov8_model.pt"
entrenar_yolo(DATA_PATH, MODELO_SALIDA,100)

New https://pypi.org/project/ultralytics/8.3.155 available  Update with 'pip install -U ultralytics'
Ultralytics 8.3.39  Python-3.10.4 torch-2.1.0+cpu CPU (Intel Core(TM) i7-10750H 2.60GHz)
engine\trainer: task=detect, mode=train, model=yolov8n.pt, data=data_other_pc.yaml, epochs=100, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=10, cache=False, device=None, workers=8, project=, name=yolov8_model9, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\yolov8_model9
Overriding model.yaml nc=80 with nc=3

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    751897  ultralytics.nn.modules.head.Detect           [3, [64, 128, 256]]           
Model summary: 225 layers, 3,011,433 parameters, 3,011,417 gradients, 8.2 GFLOPs

Transferred 319/355 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs\detect\yolov8_model9', view at http://localhost:6006/
Freezing layer 'model.22.dfl.conv.weight'

train: Scanning C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo\train\labels.cache... 55 images, 2 backgrounds, 0 corrupt: 100%|██████████| 55/55 [00:00<?, ?it/s]
val: Scanning C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo\val\labels.cache... 13 images, 0 backgrounds, 0 corrupt: 100%|██████████| 13/13 [00:00<?, ?it/s]

Plotting labels to runs\detect\yolov8_model9\labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001429, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
TensorBoard: model graph visualization added 
Image sizes 640 train, 640 val
Using 0 dataloader workers
Logging results to runs\detect\yolov8_model9
Starting training for 100 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

      1/100         0G      2.314      3.523       2.31         55        640: 100%|██████████| 4/4 [00:14<00:00,  3.60s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.62s/it]

                   all         13         53    0.00951      0.663     0.0405     0.0179

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

      2/100         0G      2.084      3.434      2.162         68        640: 100%|██████████| 4/4 [00:13<00:00,  3.44s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.40s/it]

                   all         13         53    0.00982      0.674     0.0546      0.022

# Cargar el modelo entrenado
modelo = YOLO(r"C:\Users\crome\Desktop\VIU\Code\runs\detect\yolov8_model9\weights\best.pt")

from ultralytics import YOLO
import cv2
import matplotlib.pyplot as plt

# Ruta a una imagen de prueba
imagen_prueba = r"C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo\AMB_R7_C1_2018.jpg"

# Realizar la predicción
resultados = modelo(imagen_prueba)

# Dibujar las predicciones sobre la imagen
imagen_con_predicciones = resultados[0].plot()
%matplotlib inline
# Mostrar la imagen con predicciones
plt.figure(figsize=(10, 10))
plt.imshow(cv2.cvtColor(imagen_con_predicciones, cv2.COLOR_BGR2RGB))
plt.axis("off")
plt.show()

image 1/1 C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo\AMB_R7_C1_2018.jpg: 544x640 4 Industrias, 3 Poblacions, 115.4ms
Speed: 0.0ms preprocess, 115.4ms inference, 2.0ms postprocess per image at shape (1, 3, 544, 640)

# Validar el modelo en el conjunto de validación
modelo.val(data=r"data_other_pc.yaml")

Ultralytics 8.3.39  Python-3.10.4 torch-2.1.0+cpu CPU (Intel Core(TM) i7-10750H 2.60GHz)

val: Scanning C:\Users\crome\Desktop\VIU\Code\Datos\AMB\rgb_yolo\val\labels.cache... 13 images, 0 backgrounds, 0 corrupt: 100%|██████████| 13/13 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.10it/s]

                   all         13         53      0.369      0.393      0.299       0.12
              Agricola          3          7       0.38      0.286      0.306      0.105
             Industria         10         21      0.297      0.333      0.192     0.0785
             Poblacion         12         25      0.431       0.56      0.399      0.175
Speed: 1.7ms preprocess, 55.1ms inference, 0.0ms loss, 5.9ms postprocess per image
Results saved to runs\detect\val5

ultralytics.utils.metrics.DetMetrics object with attributes:

ap_class_index: array([0, 1, 2])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x00000275AAB58580>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,
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           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,
           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,
           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,
           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,
           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,
           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,
           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,
           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,
           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,
           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.85714,     0.85714,     0.85714, ...,           0,           0,           0],
       [    0.80952,     0.80952,     0.80952, ...,           0,           0,           0],
       [       0.96,        0.96,        0.96, ...,           0,           0,           0]]), 'Confidence', 'Recall']]
fitness: 0.1376391831270174
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([    0.10532,    0.078458,     0.17549])
names: {0: 'Agricola', 1: 'Industria', 2: 'Poblacion'}
plot: True
results_dict: {'metrics/precision(B)': 0.36948484987296576, 'metrics/recall(B)': 0.39301587301587304, 'metrics/mAP50(B)': 0.2985665179364814, 'metrics/mAP50-95(B)': 0.11975836814818805, 'fitness': 0.1376391831270174}
save_dir: WindowsPath('runs/detect/val5')
speed: {'preprocess': 1.7213087815504808, 'inference': 55.0541510948768, 'loss': 0.0, 'postprocess': 5.864400130051832}
task: 'detect'

import numpy as np
import rasterio
import os
from glob import glob
from skimage.transform import resize
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os

IMAGENES_DIR = r"C:/Users/crome/Desktop/VIU/Code/Datos/AMB_TEMP/images"
MASCARAS_DIR = r"C:/Users/crome/Desktop/VIU/Code/Datos/AMB_TEMP/masks"
OUT_YOLO_DIR = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB_TEMP/npy_files/"

years = [2020, 2022, 2024]

for year in years:

    folder = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB_"+str(year)+"/"
    tif_files = sorted(glob("C:/Users/crome/Desktop/VIU/Code/Datos/AMB_"+str(year)+"/*.tif"))

    os.makedirs(IMAGENES_DIR, exist_ok=True)
    os.makedirs(MASCARAS_DIR, exist_ok=True)
    os.makedirs(OUT_YOLO_DIR, exist_ok=True)

    # u-net:
    target_rgb_shape = (256, 256, 3)
    target_mask_shape = (256, 256)

    errores = []

    for tif_path in tif_files:
        name = os.path.basename(tif_path).replace('.tif', '')

        try:
            with rasterio.open(tif_path) as src:
                img = src.read()

            rgb = np.stack([img[0], img[1], img[2]], axis=-1).astype(np.float32) / 10000.0
            mask = img[3].astype(np.uint8)

            # Redimensionar
            rgb = resize(rgb, target_rgb_shape, preserve_range=True, anti_aliasing=True)
            mask = resize(mask, target_mask_shape, order=0, preserve_range=True).astype(np.uint8)

            np.save(os.path.join(IMAGENES_DIR, f'{name}_rgb.npy'), rgb)
            np.save(os.path.join(MASCARAS_DIR, f'{name}_mask.npy'), mask)

        except Exception as e:
            print(f"Error procesando {name}: {e}")
            errores.append(name)

    print("Conversión completada.")
    if errores:
        print("Errores en archivos:", errores)

    # Yolo:

    for tif_file in sorted(glob(os.path.join(folder, 'AMB_*.tif'))):
        with rasterio.open(tif_file) as src:
            img = src.read([1, 2, 3])  # B4, B3, B2 (ajustar si es Sentinel)
            img = np.clip(img, 0, 10000) / 10000.0
            img = (img.transpose(1, 2, 0) * 255).astype(np.uint8)

            name = os.path.basename(tif_file).replace('.tif', '.jpg')
            Image.fromarray(img).save(os.path.join(OUT_YOLO_DIR, name))

Conversión completada.
Conversión completada.
Conversión completada.

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.models import load_model
from ultralytics import YOLO
import os
import cv2
from PIL import Image

# Cargar modelos
model_unet = load_model("mejor_modelo.h5")
modelo_yolo = YOLO(r"C:\Users\crome\Desktop\VIU\Code\runs\detect\yolov8_model9\weights\best.pt")


def predecir_unet(img):
    if img.max() > 1: img = img / 10000.0
    pred = model_unet.predict(np.expand_dims(img, axis=0))[0]
    return np.argmax(pred, axis=-1)

def predecir_yolo(imagen_path):
    resultados = modelo_yolo(imagen_path)
    return resultados[0]


fechas = ["2020", "2022", "2024"]
zona = "R7_C5"

# Ruta de imágenes .npy y .jpg
RGB_NPY_DIR = r"C:/Users/crome/Desktop/VIU/Code/Datos/AMB_TEMP/images"
RGB_JPG_DIR = "C:/Users/crome/Desktop/VIU/Code/Datos/AMB_TEMP/npy_files/"

# Cargar imágenes .npy para U-Net
imagenes_rgb = {
    year: np.load(os.path.join(RGB_NPY_DIR, f"AMB_{zona}_{year}_rgb.npy"))
    for year in fechas
}

# Visualización conjunta
fig, axs = plt.subplots(len(fechas), 3, figsize=(16, 5 * len(fechas)))

for i, year in enumerate(fechas):
    # 1. Imagen RGB
    rgb = imagenes_rgb[year]

    # 2. Predicción U-Net
    pred_mask = predecir_unet(rgb)

    # 3. Predicción YOLO
    imagen_jpg_path = os.path.join(RGB_JPG_DIR, f"AMB_{zona}_{year}.jpg")
    pred_yolo = predecir_yolo(imagen_jpg_path)

    # Mostrar
    axs[i, 0].imshow(rgb)
    axs[i, 0].set_title(f"RGB - {year}")
    axs[i, 0].axis('off')

    axs[i, 1].imshow(pred_mask, cmap='tab20', interpolation='none')
    axs[i, 1].set_title(f"U-Net - Segmentación ({year})")
    axs[i, 1].axis('off')

    axs[i, 2].imshow(pred_yolo.plot())
    axs[i, 2].set_title(f"YOLOv8 - Detección ({year})")
    axs[i, 2].axis('off')

plt.tight_layout()
plt.show()

1/1 [==============================] - 1s 617ms/step

image 1/1 C:\Users\crome\Desktop\VIU\Code\Datos\AMB_TEMP\npy_files\AMB_R7_C5_2020.jpg: 544x640 1 Agricola, 4 Industrias, 3 Poblacions, 316.4ms
Speed: 12.1ms preprocess, 316.4ms inference, 12.0ms postprocess per image at shape (1, 3, 544, 640)
1/1 [==============================] - 0s 184ms/step

image 1/1 C:\Users\crome\Desktop\VIU\Code\Datos\AMB_TEMP\npy_files\AMB_R7_C5_2022.jpg: 544x640 1 Agricola, 4 Industrias, 3 Poblacions, 132.7ms
Speed: 8.1ms preprocess, 132.7ms inference, 0.0ms postprocess per image at shape (1, 3, 544, 640)
1/1 [==============================] - 0s 187ms/step

image 1/1 C:\Users\crome\Desktop\VIU\Code\Datos\AMB_TEMP\npy_files\AMB_R7_C5_2024.jpg: 544x640 1 Agricola, 3 Industrias, 3 Poblacions, 210.6ms
Speed: 6.0ms preprocess, 210.6ms inference, 2.0ms postprocess per image at shape (1, 3, 544, 640)

def comparar_segmentacion(mask1, mask2):
    cambio = mask1 != mask2
    total = cambio.size
    n_cambiado = np.sum(cambio)
    porcentaje_cambiado = 100 * n_cambiado / total
    return n_cambiado, porcentaje_cambiado

# Comparaciones entre años
cambios_unet = {}
for y1, y2 in [("2020", "2022"), ("2022", "2024"), ("2020", "2024")]:
    m1 = predecir_unet(imagenes_rgb[y1])
    m2 = predecir_unet(imagenes_rgb[y2])
    n, p = comparar_segmentacion(m1, m2)
    cambios_unet[f"{y1}-{y2}"] = {"px_cambiados": n, "porcentaje": round(p, 2)}

# Mostrar resultados
print("Cambios en segmentación (U-Net):")
for k, v in cambios_unet.items():
    print(f"- {k}: {v['px_cambiados']} píxeles cambiados ({v['porcentaje']}%)")


from collections import Counter

# Inicializar diccionario
conteo_yolo = {}

for year in fechas:
    jpg_path = os.path.join(RGB_JPG_DIR, f"AMB_{zona}_{year}.jpg")
    resultado = predecir_yolo(jpg_path)

    clases = resultado.names
    ids_detectados = [int(cls) for cls in resultado.boxes.cls.cpu().numpy()]
    conteo = Counter(ids_detectados)

    # Guardamos con nombre de clase
    conteo_yolo[year] = {clases[k]: v for k, v in conteo.items()}

# Mostrar
print("Conteo YOLO por clase:")
for year in conteo_yolo:
    print(f"\n🗓 Año {year}:")
    for clase, count in conteo_yolo[year].items():
        print(f"- {clase}: {count} detecciones")

1/1 [==============================] - 0s 357ms/step
1/1 [==============================] - 0s 357ms/step
1/1 [==============================] - 0s 152ms/step
1/1 [==============================] - 0s 153ms/step
1/1 [==============================] - 0s 156ms/step
1/1 [==============================] - 0s 135ms/step
1/1 [==============================] - 0s 113ms/step
Cambios en segmentación (U-Net):
- 2020-2022: 3016 píxeles cambiados (4.6%)
- 2022-2024: 2872 píxeles cambiados (4.38%)
- 2020-2024: 3284 píxeles cambiados (5.01%)

image 1/1 C:\Users\crome\Desktop\VIU\Code\Datos\AMB_TEMP\npy_files\AMB_R7_C5_2020.jpg: 544x640 1 Agricola, 4 Industrias, 3 Poblacions, 92.2ms
Speed: 4.3ms preprocess, 92.2ms inference, 2.0ms postprocess per image at shape (1, 3, 544, 640)

image 1/1 C:\Users\crome\Desktop\VIU\Code\Datos\AMB_TEMP\npy_files\AMB_R7_C5_2022.jpg: 544x640 1 Agricola, 4 Industrias, 3 Poblacions, 207.0ms
Speed: 4.2ms preprocess, 207.0ms inference, 2.0ms postprocess per image at shape (1, 3, 544, 640)

image 1/1 C:\Users\crome\Desktop\VIU\Code\Datos\AMB_TEMP\npy_files\AMB_R7_C5_2024.jpg: 544x640 1 Agricola, 3 Industrias, 3 Poblacions, 104.6ms
Speed: 2.0ms preprocess, 104.6ms inference, 0.0ms postprocess per image at shape (1, 3, 544, 640)
Conteo YOLO por clase:

🗓 Año 2020:
- Industria: 4 detecciones
- Poblacion: 3 detecciones
- Agricola: 1 detecciones

🗓 Año 2022:
- Industria: 4 detecciones
- Poblacion: 3 detecciones
- Agricola: 1 detecciones

🗓 Año 2024:
- Industria: 3 detecciones
- Poblacion: 3 detecciones
- Agricola: 1 detecciones

import matplotlib.pyplot as plt

def calcular_ict(unet_cambios, yolo_conteos, clases_objetivo=["Población", "Industria", "Agrícola"]):
    comparaciones = list(unet_cambios.keys())
    indice_compuesto = {}

    for comp in comparaciones:
        y1, y2 = comp.split("-")
        
        cambio_pct = unet_cambios[comp]["porcentaje"]
        cambio_norm = cambio_pct / 100
        
        yolo_y1 = sum(yolo_conteos[y1].get(cl, 0) for cl in clases_objetivo)
        yolo_y2 = sum(yolo_conteos[y2].get(cl, 0) for cl in clases_objetivo)
        yolo_diff = abs(yolo_y2 - yolo_y1)
        yolo_total = max(yolo_y1, yolo_y2, 1)
        yolo_norm = yolo_diff / yolo_total

        ictt = round((cambio_norm + yolo_norm) / 2, 3)

        indice_compuesto[comp] = {
            "cambio_pct": cambio_pct,
            "yolo_diff": yolo_diff,
            "cambio_norm": cambio_norm,
            "yolo_norm": yolo_norm,
            "ICTT": ictt
        }

    return indice_compuesto

# Uso:
ictt_resultado = calcular_ict(cambios_unet, conteo_yolo)

# Mostrar
print("\nÍndice Compuesto de Transformación Territorial (ICTT):")
for comp, v in ictt_resultado.items():
    print(f"- {comp}: {v['ICTT']} (U-Net: {v['cambio_pct']}%, YOLO Δ: {v['yolo_diff']})")

Índice Compuesto de Transformación Territorial (ICTT):
- 2020-2022: 0.023 (U-Net: 4.6%, YOLO Δ: 0)
- 2022-2024: 0.147 (U-Net: 4.38%, YOLO Δ: 1)
- 2020-2024: 0.15 (U-Net: 5.01%, YOLO Δ: 1)

# Visualizar como gráfico
comparaciones = list(ictt_resultado.keys())
unet_values = [ictt_resultado[c]["cambio_norm"] for c in comparaciones]
yolo_values = [ictt_resultado[c]["yolo_norm"] for c in comparaciones]
ictt_values = [ictt_resultado[c]["ICTT"] for c in comparaciones]

x = range(len(comparaciones))
width = 0.25

plt.figure(figsize=(10, 6))
plt.bar([i - width for i in x], unet_values, width=width, label="Cambio U-Net (normalizado)", color="#FFB000")
plt.bar(x, yolo_values, width=width, label="Cambio YOLO (normalizado)", color="#FF6F00")
plt.bar([i + width for i in x], ictt_values, width=width, label="ICTT (Índice Compuesto)", color="#C70039")

plt.xticks(x, comparaciones)
plt.ylabel("Valor normalizado")
plt.title("Índice Compuesto de Transformación Territorial (ICTT)")
plt.legend()
plt.grid(axis="y", linestyle="--", alpha=0.5)
plt.tight_layout()
plt.show()

TFG - VIU - Carlos Romero Matarin¶

MODELOS¶

YOLO¶

Datos Temporales¶

	archivo	clase	pixeles	porcentaje
0	AMB_R0_C1_2018.tif	0	122386	49.07
1	AMB_R0_C1_2018.tif	100	75	0.03
2	AMB_R0_C1_2018.tif	211	874	0.35
3	AMB_R0_C1_2018.tif	212	929	0.37
4	AMB_R0_C1_2018.tif	213	2091	0.84

clase	0	100	211	212	213	214	215	216	221	223	230	231	232	233	240	250	290	300	500	600
archivo
AMB_R0_C1_2018.tif	49.07	0.03	0.35	0.37	0.84	0.0	0.00	0.48	0.01	0.0	0.36	0.32	0.00	0.0	1.13	0.85	5.38	35.24	5.56	0.01
AMB_R0_C2_2018.tif	90.05	0.00	0.01	0.01	0.01	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.0	0.00	0.03	0.12	8.18	1.58	0.00
AMB_R0_C3_2018.tif	98.82	0.00	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.01	1.03	0.14	0.00
AMB_R0_C4_2018.tif	100.00	0.00	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00
AMB_R0_C5_2018.tif	100.00	0.00	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
AMB_R9_C5_2018.tif	18.33	0.02	0.61	0.53	7.15	0.0	0.07	0.01	0.00	0.0	0.00	0.03	0.65	0.0	0.70	1.69	0.64	63.38	6.19	0.00
AMB_R9_C6_2018.tif	31.73	0.28	1.30	0.88	8.90	0.0	0.00	0.01	0.00	0.0	0.00	0.03	0.38	0.0	1.29	1.97	1.25	46.11	5.87	0.00
AMB_R9_C7_2018.tif	9.15	0.06	1.17	0.75	10.99	0.0	0.01	0.03	0.00	0.0	0.00	0.00	0.15	0.0	0.59	3.28	1.29	64.86	7.68	0.00
AMB_R9_C8_2018.tif	18.79	0.05	0.70	0.51	4.06	0.0	0.00	0.01	0.00	0.0	0.00	0.00	0.20	0.0	0.57	1.84	0.94	64.70	7.63	0.00
AMB_R9_C9_2018.tif	9.51	0.03	0.31	0.26	1.76	0.0	0.00	0.02	0.00	0.0	0.00	0.01	0.02	0.0	0.09	0.70	0.45	83.75	3.09	0.00

	clase	0	100	211	212	213	214	215	216	221	223	230	231	232	233	240	250	290	300	500	600
celda	anio
R0_C1	2018	49.07	0.03	0.35	0.37	0.84	0.0	0.0	0.48	0.01	0.0	0.36	0.32	0.0	0.0	1.13	0.85	5.38	35.24	5.56	0.01
R0_C2	2018	90.05	0.00	0.01	0.01	0.01	0.0	0.0	0.00	0.00	0.0	0.00	0.00	0.0	0.0	0.00	0.03	0.12	8.18	1.58	0.00
R0_C3	2018	98.82	0.00	0.00	0.00	0.00	0.0	0.0	0.00	0.00	0.0	0.00	0.00	0.0	0.0	0.00	0.00	0.01	1.03	0.14	0.00
R0_C4	2018	100.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	0.00	0.0	0.00	0.00	0.0	0.0	0.00	0.00	0.00	0.00	0.00	0.00
R0_C5	2018	100.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	0.00	0.0	0.00	0.00	0.0	0.0	0.00	0.00	0.00	0.00	0.00	0.00